{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d77ab740",
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2024-11-19T08:55:01.168824Z",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "! pip install -q umap-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26ccb9e5",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing loss.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile loss.py\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class LabelDifference(nn.Module):\n",
    "    def __init__(self, distance_type=\"l1\"):\n",
    "        super(LabelDifference, self).__init__()\n",
    "        self.distance_type = distance_type\n",
    "\n",
    "    def forward(self, labels):\n",
    "        # labels: [bs, label_dim]\n",
    "        # output: [bs, bs]\n",
    "        if self.distance_type == \"l1\":\n",
    "            return torch.abs(labels[:, None, :] - labels[None, :, :]).sum(dim=-1)\n",
    "        else:\n",
    "            raise ValueError(self.distance_type)\n",
    "\n",
    "\n",
    "class FeatureSimilarity(nn.Module):\n",
    "    def __init__(self, similarity_type=\"l2\"):\n",
    "        super(FeatureSimilarity, self).__init__()\n",
    "        self.similarity_type = similarity_type\n",
    "\n",
    "    def forward(self, features):\n",
    "        # labels: [bs, feat_dim]\n",
    "        # output: [bs, bs]\n",
    "        if self.similarity_type == \"l2\":\n",
    "            return -(features[:, None, :] - features[None, :, :]).norm(2, dim=-1)\n",
    "        else:\n",
    "            raise ValueError(self.similarity_type)\n",
    "\n",
    "\n",
    "class RnCLoss(nn.Module):\n",
    "    def __init__(self, temperature=2, label_diff=\"l1\", feature_sim=\"l2\"):\n",
    "        super(RnCLoss, self).__init__()\n",
    "        self.t = temperature\n",
    "        self.label_diff_fn = LabelDifference(label_diff)\n",
    "        self.feature_sim_fn = FeatureSimilarity(feature_sim)\n",
    "\n",
    "    def forward(self, features, labels):\n",
    "        # features: [bs, 2, feat_dim]\n",
    "        # labels: [bs, label_dim]\n",
    "\n",
    "        features = torch.cat([features[:, 0], features[:, 1]], dim=0)  # [2bs, feat_dim]\n",
    "        labels = labels.repeat(2, 1)  # [2bs, label_dim]\n",
    "\n",
    "        label_diffs = self.label_diff_fn(labels)\n",
    "        logits = self.feature_sim_fn(features).div(self.t)\n",
    "        logits_max, _ = torch.max(logits, dim=1, keepdim=True)\n",
    "        logits -= logits_max.detach()\n",
    "        exp_logits = logits.exp()\n",
    "\n",
    "        n = logits.shape[0]  # n = 2bs\n",
    "\n",
    "        # remove diagonal\n",
    "        logits = logits.masked_select((1 - torch.eye(n).to(logits.device)).bool()).view(\n",
    "            n, n - 1\n",
    "        )\n",
    "        exp_logits = exp_logits.masked_select(\n",
    "            (1 - torch.eye(n).to(logits.device)).bool()\n",
    "        ).view(n, n - 1)\n",
    "        label_diffs = label_diffs.masked_select(\n",
    "            (1 - torch.eye(n).to(logits.device)).bool()\n",
    "        ).view(n, n - 1)\n",
    "\n",
    "        loss = 0.0\n",
    "        for k in range(n - 1):\n",
    "            pos_logits = logits[:, k]  # 2bs\n",
    "            pos_label_diffs = label_diffs[:, k]  # 2bs\n",
    "            neg_mask = (\n",
    "                label_diffs >= pos_label_diffs.view(-1, 1)\n",
    "            ).float()  # [2bs, 2bs - 1]\n",
    "            pos_log_probs = pos_logits - torch.log(\n",
    "                (neg_mask * exp_logits).sum(dim=-1)\n",
    "            )  # 2bs\n",
    "            loss += -(pos_log_probs / (n * (n - 1))).sum()\n",
    "\n",
    "        return loss\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a133606",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T08:55:10.959623Z",
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     "status": "completed"
    },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing convnext1d.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile convnext1d.py\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from timm.layers import DropPath, trunc_normal_\n",
    "\n",
    "\n",
    "class Block(nn.Module):\n",
    "    def __init__(self, dim, drop_path=0.0, layer_scale_init_value=1e-6):\n",
    "        super().__init__()\n",
    "        self.dwconv = nn.Conv1d(\n",
    "            dim, dim, kernel_size=7, padding=3, groups=dim\n",
    "        )  # depthwise conv\n",
    "        self.norm = LayerNorm(dim, eps=1e-6)\n",
    "        self.pwconv1 = nn.Linear(\n",
    "            dim, 4 * dim\n",
    "        )  # pointwise conv, implemented with linear layers\n",
    "        self.act = nn.GELU()\n",
    "        self.pwconv2 = nn.Linear(4 * dim, dim)\n",
    "        self.gamma = (\n",
    "            nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)\n",
    "            if layer_scale_init_value > 0\n",
    "            else None\n",
    "        )\n",
    "        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n",
    "\n",
    "    def forward(self, x):\n",
    "        input = x\n",
    "        x = self.dwconv(x)\n",
    "        x = x.permute(0, 2, 1)  # (N, C, W) -> (N, W, C)\n",
    "        x = self.norm(x)\n",
    "        x = self.pwconv1(x)\n",
    "        x = self.act(x)\n",
    "        x = self.pwconv2(x)\n",
    "        if self.gamma is not None:\n",
    "            x = self.gamma * x\n",
    "        x = x.permute(0, 2, 1)  # (N, W, C) -> (N, C, W)\n",
    "        x = input + self.drop_path(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class ConvNeXt(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        in_chans=1,\n",
    "        num_classes=1000,\n",
    "        depths=[3, 3, 9, 3],\n",
    "        dims=[96, 192, 384, 768],\n",
    "        drop_path_rate=0.0,\n",
    "        layer_scale_init_value=1e-6,\n",
    "        head_init_scale=1.0,\n",
    "    ):\n",
    "        super().__init__()\n",
    "\n",
    "        self.downsample_layers = (\n",
    "            nn.ModuleList()\n",
    "        )  # stem and 3 intermediate downsampling conv layers\n",
    "        stem = nn.Sequential(\n",
    "            nn.Conv1d(in_chans, dims[0], kernel_size=4, stride=4),\n",
    "            LayerNorm(dims[0], eps=1e-6, data_format=\"channels_first\"),\n",
    "        )\n",
    "        self.downsample_layers.append(stem)\n",
    "        for i in range(3):\n",
    "            downsample_layer = nn.Sequential(\n",
    "                LayerNorm(dims[i], eps=1e-6, data_format=\"channels_first\"),\n",
    "                nn.Conv1d(dims[i], dims[i + 1], kernel_size=2, stride=2),\n",
    "            )\n",
    "            self.downsample_layers.append(downsample_layer)\n",
    "\n",
    "        self.stages = (\n",
    "            nn.ModuleList()\n",
    "        )  # 4 feature resolution stages, each consisting of multiple residual blocks\n",
    "        dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]\n",
    "        cur = 0\n",
    "        for i in range(4):\n",
    "            stage = nn.Sequential(\n",
    "                *[\n",
    "                    Block(\n",
    "                        dim=dims[i],\n",
    "                        drop_path=dp_rates[cur + j],\n",
    "                        layer_scale_init_value=layer_scale_init_value,\n",
    "                    )\n",
    "                    for j in range(depths[i])\n",
    "                ]\n",
    "            )\n",
    "            self.stages.append(stage)\n",
    "            cur += depths[i]\n",
    "\n",
    "        self.norm = nn.LayerNorm(dims[-1], eps=1e-6)  # final norm layer\n",
    "        self.head = nn.Linear(dims[-1], num_classes)\n",
    "\n",
    "        self.apply(self._init_weights)\n",
    "        self.head.weight.data.mul_(head_init_scale)\n",
    "        self.head.bias.data.mul_(head_init_scale)\n",
    "\n",
    "    def _init_weights(self, m):\n",
    "        if isinstance(m, (nn.Conv1d, nn.Linear)):\n",
    "            trunc_normal_(m.weight, std=0.02)\n",
    "            nn.init.constant_(m.bias, 0)\n",
    "\n",
    "    def forward_features(self, x):\n",
    "        for i in range(4):\n",
    "            x = self.downsample_layers[i](x)\n",
    "            x = self.stages[i](x)\n",
    "        return self.norm(x.mean(-1))  # global average pooling, (N, C, W) -> (N, C)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.forward_features(x)\n",
    "        x = self.head(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class LayerNorm(nn.Module):\n",
    "    def __init__(self, normalized_shape, eps=1e-6, data_format=\"channels_last\"):\n",
    "        super().__init__()\n",
    "        self.weight = nn.Parameter(torch.ones(normalized_shape))\n",
    "        self.bias = nn.Parameter(torch.zeros(normalized_shape))\n",
    "        self.eps = eps\n",
    "        self.data_format = data_format\n",
    "        if self.data_format not in [\"channels_last\", \"channels_first\"]:\n",
    "            raise NotImplementedError\n",
    "        self.normalized_shape = (normalized_shape,)\n",
    "\n",
    "    def forward(self, x):\n",
    "        if self.data_format == \"channels_last\":\n",
    "            return F.layer_norm(\n",
    "                x, self.normalized_shape, self.weight, self.bias, self.eps\n",
    "            )\n",
    "        elif self.data_format == \"channels_first\":\n",
    "            u = x.mean(1, keepdim=True)\n",
    "            s = (x - u).pow(2).mean(1, keepdim=True)\n",
    "            x = (x - u) / torch.sqrt(s + self.eps)\n",
    "            x = self.weight[:, None] * x + self.bias[:, None]\n",
    "            return x\n",
    "\n",
    "\n",
    "def convnext_tiny(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)\n",
    "    return model\n",
    "\n",
    "\n",
    "def convnext_small(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)\n",
    "    return model\n",
    "\n",
    "\n",
    "def convnext_base(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)\n",
    "    return model\n",
    "\n",
    "\n",
    "def convnext_large(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)\n",
    "    return model\n",
    "\n",
    "\n",
    "def convnext_xlarge(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7f7e1e0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T08:55:10.975064Z",
     "iopub.status.busy": "2024-11-19T08:55:10.974785Z",
     "iopub.status.idle": "2024-11-19T08:55:10.981509Z",
     "shell.execute_reply": "2024-11-19T08:55:10.980681Z"
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing convnext2d.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile convnext2d.py\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from timm.layers import DropPath, trunc_normal_\n",
    "\n",
    "class Block(nn.Module):\n",
    "    def __init__(self, dim, drop_path=0.0, layer_scale_init_value=1e-6):\n",
    "        super().__init__()\n",
    "        self.dwconv = nn.Conv2d(\n",
    "            dim, dim, kernel_size=7, padding=3, groups=dim\n",
    "        )  # depthwise conv (2D)\n",
    "        self.norm = LayerNorm(dim, eps=1e-6)\n",
    "        self.pwconv1 = nn.Linear(\n",
    "            dim, 4 * dim\n",
    "        )  # pointwise conv, implemented with linear layers\n",
    "        self.act = nn.GELU()\n",
    "        self.pwconv2 = nn.Linear(4 * dim, dim)\n",
    "        self.gamma = (\n",
    "            nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)\n",
    "            if layer_scale_init_value > 0\n",
    "            else None\n",
    "        )\n",
    "        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n",
    "\n",
    "    def forward(self, x):\n",
    "        input = x\n",
    "        x = self.dwconv(x)\n",
    "        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)\n",
    "        x = self.norm(x)\n",
    "        x = self.pwconv1(x)\n",
    "        x = self.act(x)\n",
    "        x = self.pwconv2(x)\n",
    "        if self.gamma is not None:\n",
    "            x = self.gamma * x\n",
    "        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)\n",
    "        x = input + self.drop_path(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class ConvNeXt(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        in_chans=1,\n",
    "        num_classes=1000,\n",
    "        depths=[3, 3, 9, 3],\n",
    "        dims=[96, 192, 384, 768],\n",
    "        drop_path_rate=0.0,\n",
    "        layer_scale_init_value=1e-6,\n",
    "        head_init_scale=1.0,\n",
    "    ):\n",
    "        super().__init__()\n",
    "\n",
    "        self.downsample_layers = (\n",
    "            nn.ModuleList()\n",
    "        )  # stem and 3 intermediate downsampling conv layers\n",
    "        stem = nn.Sequential(\n",
    "            nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),  # 2D Conv\n",
    "            LayerNorm(dims[0], eps=1e-6, data_format=\"channels_first\"),\n",
    "        )\n",
    "        self.downsample_layers.append(stem)\n",
    "        for i in range(3):\n",
    "            downsample_layer = nn.Sequential(\n",
    "                LayerNorm(dims[i], eps=1e-6, data_format=\"channels_first\"),\n",
    "                nn.Conv2d(dims[i], dims[i + 1], kernel_size=2, stride=2),  # 2D Conv\n",
    "            )\n",
    "            self.downsample_layers.append(downsample_layer)\n",
    "\n",
    "        self.stages = (\n",
    "            nn.ModuleList()\n",
    "        )  # 4 feature resolution stages, each consisting of multiple residual blocks\n",
    "        dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]\n",
    "        cur = 0\n",
    "        for i in range(4):\n",
    "            stage = nn.Sequential(\n",
    "                *[\n",
    "                    Block(\n",
    "                        dim=dims[i],\n",
    "                        drop_path=dp_rates[cur + j],\n",
    "                        layer_scale_init_value=layer_scale_init_value,\n",
    "                    )\n",
    "                    for j in range(depths[i])\n",
    "                ]\n",
    "            )\n",
    "            self.stages.append(stage)\n",
    "            cur += depths[i]\n",
    "\n",
    "        self.norm = nn.LayerNorm(dims[-1], eps=1e-6)  # final norm layer\n",
    "        self.head = nn.Linear(dims[-1], num_classes)\n",
    "\n",
    "        self.apply(self._init_weights)\n",
    "        self.head.weight.data.mul_(head_init_scale)\n",
    "        self.head.bias.data.mul_(head_init_scale)\n",
    "\n",
    "    def _init_weights(self, m):\n",
    "        if isinstance(m, (nn.Conv2d, nn.Linear)):\n",
    "            trunc_normal_(m.weight, std=0.02)\n",
    "            nn.init.constant_(m.bias, 0)\n",
    "\n",
    "    def forward_features(self, x):\n",
    "        # Assuming x shape is (N, C, H, W)\n",
    "        for i in range(4):\n",
    "            x = self.downsample_layers[i](x)\n",
    "            x = self.stages[i](x)\n",
    "        return self.norm(x.mean(-1))  # global average pooling, (N, C, H, W) -> (N, C)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.forward_features(x)\n",
    "        x = self.head(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class LayerNorm(nn.Module):\n",
    "    def __init__(self, normalized_shape, eps=1e-6, data_format=\"channels_last\"):\n",
    "        super().__init__()\n",
    "        self.weight = nn.Parameter(torch.ones(normalized_shape))\n",
    "        self.bias = nn.Parameter(torch.zeros(normalized_shape))\n",
    "        self.eps = eps\n",
    "        self.data_format = data_format\n",
    "        if self.data_format not in [\"channels_last\", \"channels_first\"]:\n",
    "            raise NotImplementedError\n",
    "        self.normalized_shape = (normalized_shape,)\n",
    "\n",
    "    def forward(self, x):\n",
    "        if self.data_format == \"channels_last\":\n",
    "            return F.layer_norm(\n",
    "                x, self.normalized_shape, self.weight, self.bias, self.eps\n",
    "            )\n",
    "        elif self.data_format == \"channels_first\":\n",
    "            u = x.mean(1, keepdim=True)\n",
    "            s = (x - u).pow(2).mean(1, keepdim=True)\n",
    "            x = (x - u) / torch.sqrt(s + self.eps)\n",
    "            x = self.weight[:, None] * x + self.bias[:, None]\n",
    "            return x\n",
    "\n",
    "\n",
    "def convnext_tiny(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)\n",
    "    return model\n",
    "\n",
    "\n",
    "def convnext_small(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)\n",
    "    return model\n",
    "\n",
    "\n",
    "def convnext_base(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)\n",
    "    return model\n",
    "\n",
    "\n",
    "def convnext_large(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)\n",
    "    return model\n",
    "\n",
    "\n",
    "def convnext_xlarge(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b0d677d",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing ppg_dalia.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile ppg_dalia.py\n",
    "import glob\n",
    "import os\n",
    "\n",
    "import mne\n",
    "import numpy as np\n",
    "import torch\n",
    "from scipy.signal import spectrogram\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch.utils.data import Dataset\n",
    "\n",
    "data_dir = \"/kaggle/input/ppg-dalia-processed\"\n",
    "filepaths = glob.glob(os.path.join(data_dir, \"*.npz\"))\n",
    "\n",
    "# 80%-10%-10%\n",
    "train_files, test_files = train_test_split(\n",
    "    filepaths,\n",
    "    test_size=0.2,\n",
    "    shuffle=True,\n",
    "    random_state=42,\n",
    ")\n",
    "val_files, test_files = train_test_split(\n",
    "    test_files,\n",
    "    test_size=0.5,\n",
    "    shuffle=True,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "files = {\n",
    "    \"train\": train_files,\n",
    "    \"val\": val_files,\n",
    "    \"test\": test_files,\n",
    "}\n",
    "ecgs = {\"train\": [], \"val\": [], \"test\": []}\n",
    "ppgs = {\"train\": [], \"val\": [], \"test\": []}\n",
    "\n",
    "labels = {\"train\": [], \"val\": [], \"test\": []}\n",
    "\n",
    "for split in [\"train\", \"val\", \"test\"]:\n",
    "    for file in files[split]:\n",
    "        data = np.load(file)\n",
    "        ecg = data[\"ecg\"]\n",
    "        ppg = data[\"ppg\"]\n",
    "        label = data[\"label\"]\n",
    "        ecg_resampled = mne.filter.resample(ecg.astype(float), down=700 / 64)\n",
    "\n",
    "        ecgs[split].append(ecg_resampled)\n",
    "        ppgs[split].append(ppg)\n",
    "        labels[split].append(label)\n",
    "\n",
    "    ecgs[split] = np.concatenate(ecgs[split], axis=0)\n",
    "    ppgs[split] = np.concatenate(ppgs[split], axis=0)\n",
    "    labels[split] = np.concatenate(labels[split], axis=0)\n",
    "\n",
    "mean, std = labels[\"train\"].mean(), labels[\"train\"].std()\n",
    "\n",
    "\n",
    "def normalize_labels(label):\n",
    "    return (label - mean) / std\n",
    "\n",
    "\n",
    "def unnormalize_labels(label):\n",
    "    return std * label + mean\n",
    "\n",
    "\n",
    "class PPG_DaLiA(Dataset):\n",
    "\n",
    "    def __init__(self, ecgs, ppgs, labels):\n",
    "        self.ecgs = spectrogram((ecgs - np.min(ppgs)) / (np.max(ecgs) - np.min(ecgs)), 64, nperseg=2)[2]\n",
    "        self.ppgs = spectrogram((ppgs - np.min(ppgs)) / (np.max(ppgs) - np.min(ppgs)), 64, nperseg=2)[2]\n",
    "        self.labels = normalize_labels(labels)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        ecg = torch.as_tensor(self.ecgs[index], dtype=torch.float32)\n",
    "        ppg = torch.as_tensor(self.ppgs[index], dtype=torch.float32)\n",
    "        label = torch.as_tensor(self.labels[index], dtype=torch.float32)\n",
    "\n",
    "        #ecg = ecg.unsqueeze(0)\n",
    "        #ppg = ppg.unsqueeze(0)\n",
    "        label = label.unsqueeze(0)\n",
    "\n",
    "        return ecg, ppg, label\n",
    "\n",
    "\n",
    "dataset = {\n",
    "    \"train\": PPG_DaLiA(ecgs[\"train\"], ppgs[\"train\"], labels[\"train\"]),\n",
    "    \"val\": PPG_DaLiA(ecgs[\"val\"], ppgs[\"val\"], labels[\"val\"]),\n",
    "    \"test\": PPG_DaLiA(ecgs[\"test\"], ppgs[\"test\"], labels[\"test\"]),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f804943",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T08:55:11.005286Z",
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     "status": "completed"
    },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model params: 1.18 M\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c957e2e0a1f542bd9f635d473385891f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/48240 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=0, Loss=3.24\n",
      "Epoch=1, Loss=3.23\n",
      "Epoch=2, Loss=3.20\n",
      "Epoch=3, Loss=3.16\n",
      "Epoch=4, Loss=3.16\n",
      "Epoch=5, Loss=3.16\n",
      "Epoch=6, Loss=3.16\n",
      "Epoch=7, Loss=3.16\n",
      "Epoch=8, Loss=3.16\n",
      "Epoch=9, Loss=3.16\n",
      "Epoch=10, Loss=3.16\n",
      "Epoch=11, Loss=3.16\n",
      "Epoch=12, Loss=3.16\n",
      "Epoch=13, Loss=3.16\n",
      "Epoch=14, Loss=3.16\n",
      "Epoch=15, Loss=3.16\n",
      "Epoch=16, Loss=3.16\n",
      "Epoch=17, Loss=3.16\n",
      "Epoch=18, Loss=3.16\n",
      "Epoch=19, Loss=3.16\n",
      "Epoch=20, Loss=3.16\n",
      "Epoch=21, Loss=3.22\n",
      "Epoch=22, Loss=3.16\n",
      "Epoch=23, Loss=3.16\n",
      "Epoch=24, Loss=3.16\n",
      "Epoch=25, Loss=3.16\n",
      "Epoch=26, Loss=3.16\n",
      "Epoch=27, Loss=3.16\n",
      "Epoch=28, Loss=3.16\n",
      "Epoch=29, Loss=3.16\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchmetrics.aggregation import MeanMetric\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from convnext1d import ConvNeXt\n",
    "from loss import RnCLoss\n",
    "from ppg_dalia import dataset, unnormalize_labels\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "batch_size = 32\n",
    "train_loader = DataLoader(dataset[\"train\"], batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(dataset[\"val\"], batch_size=batch_size)\n",
    "test_loader = DataLoader(dataset[\"test\"], batch_size=batch_size)\n",
    "\n",
    "ecg_enc = ConvNeXt(in_chans = 2, depths=[3, 3, 3, 3], dims=[24, 48, 96, 192], num_classes=1)\n",
    "ecg_enc.to(device)\n",
    "\n",
    "ppg_enc = ConvNeXt(in_chans = 2, depths=[3, 3, 3, 3], dims=[24, 48, 96, 192], num_classes=1)\n",
    "ppg_enc.to(device)\n",
    "\n",
    "model_size = 0\n",
    "for param in ppg_enc.parameters():\n",
    "    model_size += param.data.nelement()\n",
    "print(\"Model params: %.2f M\" % (model_size / 1024 / 1024))\n",
    "\n",
    "# Pretraining: Rank-N-Constrast\n",
    "epochs = 30\n",
    "lr = 3e-4\n",
    "optimizer_ppg = torch.optim.Adam(ppg_enc.parameters(), lr=lr)\n",
    "optimizer_ecg = torch.optim.Adam(ecg_enc.parameters(), lr=lr)\n",
    "criterion = RnCLoss(temperature=2, label_diff=\"l1\", feature_sim=\"l2\")\n",
    "\n",
    "avg_loss = MeanMetric()\n",
    "train_losses = []\n",
    "\n",
    "pbar = tqdm(total=epochs*len(train_loader))\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for ecgs, ppgs, labels in train_loader:\n",
    "        ecgs = ecgs.to(device)\n",
    "        ppgs = ppgs.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        ecg_feats = ecg_enc.forward_features(ecgs)\n",
    "        ppg_feats = ppg_enc.forward_features(ppgs)\n",
    "        feats = torch.stack([ecg_feats, ppg_feats], dim=1)\n",
    "        loss = criterion(feats, labels)\n",
    "\n",
    "        optimizer_ecg.zero_grad()\n",
    "        optimizer_ppg.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer_ecg.step()\n",
    "        optimizer_ppg.step()\n",
    "\n",
    "        train_losses.append(loss.item())\n",
    "        avg_loss.update(loss.item())\n",
    "        pbar.update()\n",
    "\n",
    "    print(f\"Epoch={epoch}, Loss={avg_loss.compute():.2f}\")\n",
    "    avg_loss.reset()\n",
    "\n",
    "pbar.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "98406536",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 256)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import spectrogram\n",
    "\n",
    "txt = np.arange(512)\n",
    "_,_,sxx = spectrogram(txt, 64, nperseg = 2)\n",
    "sxx.shape"
   ]
  },
  {
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_losses)\n",
    "plt.xlabel(\"Step\")\n",
    "plt.title(\"Train/Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c6c5cdaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T09:36:31.262223Z",
     "iopub.status.busy": "2024-11-19T09:36:31.261921Z",
     "iopub.status.idle": "2024-11-19T09:37:02.094925Z",
     "shell.execute_reply": "2024-11-19T09:37:02.094081Z"
    },
    "papermill": {
     "duration": 30.846086,
     "end_time": "2024-11-19T09:37:02.102195",
     "exception": false,
     "start_time": "2024-11-19T09:36:31.256109",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11753872cd764691be5ceede78f76561",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=0, MAE=15.40\n",
      "Epoch=1, MAE=17.08\n",
      "Epoch=2, MAE=16.14\n"
     ]
    }
   ],
   "source": [
    "# Fine-Tuning\n",
    "\n",
    "model = ppg_enc\n",
    "epochs = 3\n",
    "lr = 3e-4\n",
    "optimizer = torch.optim.Adam(model.head.parameters(), lr=lr)\n",
    "\n",
    "train_losses = []\n",
    "val_mae = []\n",
    "\n",
    "for epoch in tqdm(range(epochs)):\n",
    "    for _, inputs, labels in train_loader:\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "        with torch.no_grad():\n",
    "            feats = model.forward_features(inputs)\n",
    "        preds = model.head(feats)\n",
    "        loss = F.mse_loss(preds, labels)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        train_losses.append(loss.item())\n",
    "\n",
    "    batched_errors = []\n",
    "    for _, inputs, labels in val_loader:\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            preds = model(inputs)\n",
    "\n",
    "        errors = F.l1_loss(\n",
    "            unnormalize_labels(preds),\n",
    "            unnormalize_labels(labels),\n",
    "            reduction=\"none\",\n",
    "        )\n",
    "        batched_errors.append(errors.cpu().numpy())\n",
    "\n",
    "    batched_errors = np.concatenate(batched_errors, axis=0)\n",
    "    mae = np.mean(batched_errors)\n",
    "\n",
    "    val_mae.append(mae)\n",
    "    print(f\"Epoch={epoch}, MAE={mae:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5051bc38",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T09:37:02.113674Z",
     "iopub.status.busy": "2024-11-19T09:37:02.113398Z",
     "iopub.status.idle": "2024-11-19T09:37:02.365818Z",
     "shell.execute_reply": "2024-11-19T09:37:02.365037Z"
    },
    "papermill": {
     "duration": 0.260096,
     "end_time": "2024-11-19T09:37:02.367513",
     "exception": false,
     "start_time": "2024-11-19T09:37:02.107417",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_losses)\n",
    "plt.xlabel(\"Step\")\n",
    "plt.title(\"Train/Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff33bf71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T09:37:02.380097Z",
     "iopub.status.busy": "2024-11-19T09:37:02.379817Z",
     "iopub.status.idle": "2024-11-19T09:37:02.609905Z",
     "shell.execute_reply": "2024-11-19T09:37:02.609036Z"
    },
    "papermill": {
     "duration": 0.238217,
     "end_time": "2024-11-19T09:37:02.611675",
     "exception": false,
     "start_time": "2024-11-19T09:37:02.373458",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(val_mae)\n",
    "plt.xlabel(\"Step\")\n",
    "plt.title(\"Val/MAE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cbe3249b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T09:37:02.624705Z",
     "iopub.status.busy": "2024-11-19T09:37:02.624470Z",
     "iopub.status.idle": "2024-11-19T09:37:04.140540Z",
     "shell.execute_reply": "2024-11-19T09:37:04.139636Z"
    },
    "papermill": {
     "duration": 1.524529,
     "end_time": "2024-11-19T09:37:04.142281",
     "exception": false,
     "start_time": "2024-11-19T09:37:02.617752",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE: 27.75401\n"
     ]
    }
   ],
   "source": [
    "batched_errors = []\n",
    "for _, inputs, labels in test_loader:\n",
    "    inputs = inputs.to(device)\n",
    "    labels = labels.to(device)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        preds = model(inputs)\n",
    "\n",
    "    errors = F.l1_loss(\n",
    "        unnormalize_labels(preds), unnormalize_labels(labels), reduction='none',\n",
    "    )\n",
    "    batched_errors.append(errors.cpu().numpy())\n",
    "\n",
    "batched_errors = np.concatenate(batched_errors, axis=0)\n",
    "mae = np.mean(batched_errors)\n",
    "\n",
    "print(\"Test MAE:\", mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "747be8c2",
   "metadata": {
    "papermill": {
     "duration": 0.006042,
     "end_time": "2024-11-19T09:37:04.155246",
     "exception": false,
     "start_time": "2024-11-19T09:37:04.149204",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Examining Feature Space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "711d76e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T09:37:04.168549Z",
     "iopub.status.busy": "2024-11-19T09:37:04.168267Z",
     "iopub.status.idle": "2024-11-19T09:37:04.820849Z",
     "shell.execute_reply": "2024-11-19T09:37:04.819938Z"
    },
    "papermill": {
     "duration": 0.661264,
     "end_time": "2024-11-19T09:37:04.822666",
     "exception": false,
     "start_time": "2024-11-19T09:37:04.161402",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "batched_feats = []\n",
    "batched_labels = []\n",
    "for _, inputs, labels in val_loader:\n",
    "    inputs = inputs.to(device)\n",
    "    labels = labels.to(device)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        feats = model.forward_features(inputs)\n",
    "    \n",
    "    batched_feats.append(feats.cpu())\n",
    "    batched_labels.append(labels.cpu())\n",
    "    \n",
    "feats = torch.cat(batched_feats, dim=0)\n",
    "labels = torch.cat(batched_labels, dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "59b01cb3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T09:37:04.836617Z",
     "iopub.status.busy": "2024-11-19T09:37:04.835926Z",
     "iopub.status.idle": "2024-11-19T09:37:44.886850Z",
     "shell.execute_reply": "2024-11-19T09:37:44.886162Z"
    },
    "papermill": {
     "duration": 40.059804,
     "end_time": "2024-11-19T09:37:44.888878",
     "exception": false,
     "start_time": "2024-11-19T09:37:04.829074",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import umap\n",
    "\n",
    "feats_reduced = umap.UMAP().fit_transform(feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3dfbb7dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T09:37:44.904056Z",
     "iopub.status.busy": "2024-11-19T09:37:44.902722Z",
     "iopub.status.idle": "2024-11-19T09:37:45.259175Z",
     "shell.execute_reply": "2024-11-19T09:37:45.258362Z"
    },
    "papermill": {
     "duration": 0.365825,
     "end_time": "2024-11-19T09:37:45.261456",
     "exception": false,
     "start_time": "2024-11-19T09:37:44.895631",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    feats_reduced[:, 0],\n",
    "    feats_reduced[:, 1],\n",
    "    c=unnormalize_labels(labels).squeeze(),\n",
    "    cmap=\"magma\",\n",
    ")\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "gpu",
   "dataSources": [
    {
     "datasetId": 5711874,
     "sourceId": 9407429,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30787,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 2569.471491,
   "end_time": "2024-11-19T09:37:48.181150",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-11-19T08:54:58.709659",
   "version": "2.6.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "0747f6e6dbb24cd3a6739d2e45d48ab3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
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     "11753872cd764691be5ceede78f76561": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_b5c7dd55f3f645f3b87817fc20fc9cb1",
        "IPY_MODEL_e8389cac050e447ab3126be40508e6b3",
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